TY - JOUR A1 - Esch, Thomas A1 - Saupe, T. A1 - Fahl, E. A1 - Koch, F. T1 - Verbrauchseinsparung durch bedarfsgerechten Antrieb der Nebenaggregate JF - Motortechnische Zeitschrift. 55 (1994), H. 7/8 Y1 - 1994 SN - 0024-8525 SP - 416 EP - 431 ER - TY - JOUR A1 - Esch, Thomas A1 - Funke, Harald A1 - Roosen, Peter A1 - Jarolimek, Ulrich T1 - Biogene Automobilkraftstoffe in der allgemeinen Luftfahrt JF - Motortechnische Zeitschrift (MTZ). Y1 - 2011 SN - 0024-8525 U6 - https://doi.org/10.1365/s35146-011-0013-7 VL - 72 IS - 1 SP - 54 EP - 59 PB - Springer Nature CY - Basel ER - TY - JOUR A1 - Esch, Thomas T1 - Trends in der Nutzfahrzeugantriebstechnik JF - Motortechnische Zeitschrift (MTZ) Y1 - 2010 SN - 0024-8525 U6 - https://doi.org/10.1007/bf03225608 VL - 71 IS - 10 SP - 652 EP - 658 PB - Springer Nature CY - Basel ER - TY - JOUR A1 - Esch, Thomas A1 - Funke, Harald A1 - Roosen, Peter A1 - Jarolimek, Ulrich T1 - Biogenic Vehicle Fuels in General Aviation Aircrafts JF - MTZ worldwide. 72 (2011), H. 1 Y1 - 2011 N1 - recherchierbar für Angehörige der FH Aachen SP - 38 EP - 43 PB - Springer Automotive Media CY - Wiesbaden ER - TY - JOUR A1 - Kreyer, Jörg A1 - Müller, Marvin A1 - Esch, Thomas T1 - A Calculation Methodology for Predicting Exhaust Mass Flows and Exhaust Temperature Profiles for Heavy-Duty Vehicles JF - SAE International Journal of Commercial Vehicles N2 - The predictive control of commercial vehicle energy management systems, such as vehicle thermal management or waste heat recovery (WHR) systems, are discussed on the basis of information sources from the field of environment recognition and in combination with the determination of the vehicle system condition. In this article, a mathematical method for predicting the exhaust gas mass flow and the exhaust gas temperature is presented based on driving data of a heavy-duty vehicle. The prediction refers to the conditions of the exhaust gas at the inlet of the exhaust gas recirculation (EGR) cooler and at the outlet of the exhaust gas aftertreatment system (EAT). The heavy-duty vehicle was operated on the motorway to investigate the characteristic operational profile. In addition to the use of road gradient profile data, an evaluation of the continuously recorded distance signal, which represents the distance between the test vehicle and the road user ahead, is included in the prediction model. Using a Fourier analysis, the trajectory of the vehicle speed is determined for a defined prediction horizon. To verify the method, a holistic simulation model consisting of several hierarchically structured submodels has been developed. A map-based submodel of a combustion engine is used to determine the EGR and EAT exhaust gas mass flows and exhaust gas temperature profiles. All simulation results are validated on the basis of the recorded vehicle and environmental data. Deviations from the predicted values are analyzed and discussed. Y1 - 2020 U6 - https://doi.org/10.4271/02-13-02-0009 SN - 1946-3928 VL - 13 IS - 2 SP - 129 EP - 143 PB - SAE International CY - Warrendale, Pa. ER - TY - JOUR A1 - Khayyam, Hamid A1 - Jamali, Ali A1 - Bab-Hadiashar, Alireza A1 - Esch, Thomas A1 - Ramakrishna, Seeram A1 - Jalil, Mahdi A1 - Naebe, Minoo T1 - A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modelling with Application in Industry 4.0 JF - IEEE Access N2 - To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements. Y1 - 2020 SN - 2169-3536 U6 - https://doi.org/10.1109/ACCESS.2020.2999898 SP - 1 EP - 12 PB - IEEE CY - New York, NY ER - TY - JOUR A1 - Khayyam, Hamid A1 - Jamali, Ali A1 - Bab-Hadiashar, Alireza A1 - Esch, Thomas A1 - Ramakrishna, Seeram A1 - Jalili, Mahdi A1 - Naebe, Minoo T1 - A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modeling with Application in Industry 4.0 JF - IEEE Access N2 - To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements. Y1 - 2020 U6 - https://doi.org/10.1109/ACCESS.2020.2999898 SN - 2169-3536 VL - 8 IS - Art. 9108222 SP - 111381 EP - 111393 PB - IEEE CY - New York, NY ER - TY - JOUR A1 - Schopen, Oliver A1 - Narayan, Sriram A1 - Beckmann, Marvin A1 - Najmi, Aezid-Ul-Hassan A1 - Esch, Thomas A1 - Shabani, Bahman T1 - An EIS approach to quantify the effects of inlet air relative humidity on the performance of proton exchange membrane fuel cells: a pathway to developing a novel fault diagnostic method JF - International Journal of Hydrogen Energy N2 - In this work, the effect of low air relative humidity on the operation of a polymer electrolyte membrane fuel cell is investigated. An innovative method through performing in situ electrochemical impedance spectroscopy is utilised to quantify the effect of inlet air relative humidity at the cathode side on internal ionic resistances and output voltage of the fuel cell. In addition, algorithms are developed to analyse the electrochemical characteristics of the fuel cell. For the specific fuel cell stack used in this study, the membrane resistance drops by over 39 % and the cathode side charge transfer resistance decreases by 23 % after increasing the humidity from 30 % to 85 %, while the results of static operation also show an increase of ∼2.2 % in the voltage output after increasing the relative humidity from 30 % to 85 %. In dynamic operation, visible drying effects occur at < 50 % relative humidity, whereby the increase of the air side stoichiometry increases the drying effects. Furthermore, other parameters, such as hydrogen humidification, internal stack structure, and operating parameters like stoichiometry, pressure, and temperature affect the overall water balance. Therefore, the optimal humidification range must be determined by considering all these parameters to maximise the fuel cell performance and durability. The results of this study are used to develop a health management system to ensure sufficient humidification by continuously monitoring the fuel cell polarisation data and electrochemical impedance spectroscopy indicators. KW - PEM fuel cell KW - Electrochemical impedance spectroscopy KW - Relative air humidity KW - Active humidity control KW - Impedance analysis Y1 - 2024 SN - 0360-3199 (print) U6 - https://doi.org/10.1016/j.ijhydene.2024.01.218 SN - 1879-3487 (online) VL - 58 IS - 8 SP - 1302 EP - 1315 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Funke, Harald A1 - Esch, Thomas A1 - Roosen, Petra T1 - Antriebssystemanpassungen zur Verwendung von LPG als Flugkraftstoff JF - Motortechnische Zeitschrift (MTZ) N2 - Auch in der allgemeinen Luftfahrt wäre es wünschenswert, die bereits vorhandenen Verbrennungsmotoren mit weniger CO₂-trächtigen Kraftstoffen als dem heute weit verbreiteten Avgas 100LL betreiben zu können. Es ist anzunehmen, dass im Vergleich die unter Normalbedingungen gasförmigen Kraftstoffe CNG, LPG oder LNG deutlich weniger Emissionen produzieren. Erforderliche Antriebssystemanpassungen wurden im Rahmen eines Forschungsprojekts an der FH Aachen untersucht. Y1 - 2022 U6 - https://doi.org/10.1007/s35146-021-0778-2 VL - 2022 IS - 83 SP - 58 EP - 62 PB - Springer Nature CY - Basel ER - TY - JOUR A1 - Esch, Thomas T1 - Trends in commercial vehicle powertrains JF - ATZautotechnology N2 - Low emission zones and truck bans, the rising price of diesel and increases in road tolls: all of these factors are putting serious pressure on the transport industry. Commercial vehicle manufacturers and their suppliers are in the process of identifying new solutions to these challenges as part of their efforts to meet the EEV (enhanced environmentally friendly vehicle) limits, which are currently the most robust European exhaust and emissions standards for trucks and buses. KW - European Transient Cycle KW - Common Rail Injection System KW - Commercial Vehicle KW - Selective Catalytic Reduction KW - Diesel Engine Y1 - 2010 U6 - https://doi.org/10.1007/BF03247185 SN - 2192-886X VL - 2010 IS - 10 SP - 26 EP - 31 PB - Vieweg & Sohn CY - Wiesbaden ER - TY - JOUR A1 - Funke, Harald A1 - Esch, Thomas A1 - Roosen, Petra T1 - Powertrain Adaptions for LPG Usage in General Aviation JF - MTZ worldwide N2 - In general aviation, too, it is desirable to be able to operate existing internal combustion engines with fuels that produce less CO₂ than Avgas 100LL being widely used today It can be assumed that, in comparison, the fuels CNG, LPG or LNG, which are gaseous under normal conditions, produce significantly lower emissions. Necessary propulsion system adaptations were investigated as part of a research project at Aachen University of Applied Sciences. Y1 - 2022 U6 - https://doi.org/10.1007/s38313-021-0756-6 VL - 2022 IS - 83 SP - 58 EP - 62 PB - Springer Nature CY - Basel ER - TY - JOUR A1 - Schopen, Oliver A1 - Shah, Neel A1 - Esch, Thomas A1 - Shabani, Bahman T1 - Critical quantitative evaluation of integrated health management methods for fuel cell applications JF - International Journal of Hydrogen Energy N2 - Online fault diagnostics is a crucial consideration for fuel cell systems, particularly in mobile applications, to limit downtime and degradation, and to increase lifetime. Guided by a critical literature review, in this paper an overview of Health management systems classified in a scheme is presented, introducing commonly utilised methods to diagnose FCs in various applications. In this novel scheme, various Health management system methods are summarised and structured to provide an overview of existing systems including their associated tools. These systems are classified into four categories mainly focused on model-based and non-model-based systems. The individual methods are critically discussed when used individually or combined aimed at further understanding their functionality and suitability in different applications. Additionally, a tool is introduced to evaluate methods from each category based on the scheme presented. This tool applies the technique of matrix evaluation utilising several key parameters to identify the most appropriate methods for a given application. Based on this evaluation, the most suitable methods for each specific application are combined to build an integrated Health management system. KW - Fuel cell KW - Health management system KW - Online diagnostic KW - Fault detection KW - Non-model-based Evaluation Y1 - 2024 U6 - https://doi.org/10.1016/j.ijhydene.2024.05.156 SN - 0360-3199 VL - 70 SP - 370 EP - 388 PB - Elsevier CY - Amsterdam ER -